While information of high-resolution flow fields have allowed us to understand complex flow physics, uses of neural networks which account for nonlinearity into its regression procedure can also be found for data reconstruction and estimation [1]}.
For instance, Fukami et al. [2]} used a combination of convolutional neural network (CNN) and MLP to predict the temporal evolution of a cross-sectional field in a turbulent channel flow and applied the unified model as an inflow turbulence generator.
The CNN-based model was also presented by Salehipour and Peltier [3]} to predict the small scale motions in the ocean turbulence referred to as atoms.
From the perspective of image processing, super-resolution analysis, in which high-resolution data are recovered from its low-resolution counter part, was applied to turbulent flows by Fukami et al. [4]}, [5]}.
The extension of this idea to higher Reynolds number flows [6]}, experimental data [7]}, and three-dimensional turbulence [8]} can also be found.
In addition, a CNN-based velocity estimator for particle image velocimetry (PIV) was proposed by Cai et al. [9]}.
They examined its ability considering various flows.
The applicability of a similar method to deteriorated experimental images was recently investigated by Morimoto et al. [10]}.
To sum up, various methods for neural network-based fluid data enrichment were proposed for both numerical and experimental studies.
